In a significant stride toward decarbonizing power systems, researchers have developed an advanced optimization tool tailored for regions grappling with stringent carbon emission constraints. The study, led by Kai-Hung Lu of the School of Electronic and Electrical Engineering at Minnan University of Science and Technology in China, introduces an Adaptive Grouped Fish Migration Optimization (AGFMO) algorithm designed to enhance power dispatch strategies in carbon-taxed environments. Published in the journal *Mathematics*, the research offers a promising solution for regions outside international carbon trading frameworks, where local policies and system uncertainties demand adaptive, robust optimization tools.
The AGFMO algorithm stands out for its dynamic population grouping, a perturbation-assisted escape strategy from local optima, and a performance-feedback-driven position update rule. These enhancements significantly improve the algorithm’s reliability and global search capacity, making it well-suited for complex constrained environments. “Our approach not only reduces total dispatch costs but also achieves greater CO₂ emission reductions compared to conventional swarm-based techniques,” Lu explained. “This flexibility is crucial for regions navigating the complexities of carbon taxation and renewable energy integration.”
The study’s implementation in Taiwan’s 345 kV transmission system over a decadal planning horizon (2023–2033) underscores its practical relevance. By simulating scenarios with varying load demands, wind power integration levels, and carbon tax schemes, the research demonstrates the AGFMO’s effectiveness in real-world grid settings. “Embedding policy parameters directly into the optimization framework ensures robustness and adaptability, which is essential for future carbon taxation regimes,” Lu added.
The findings highlight the potential for AGFMO to serve as a decision-support tool in emission-sensitive operational planning, particularly in power markets with limited access to global carbon trading. As the energy sector continues its transition toward low-carbon systems, such advancements in control and optimization processes are critical. The research not only contributes to the advanced modeling of low-carbon energy systems but also paves the way for more efficient, cost-effective, and environmentally friendly power dispatch strategies. By bridging the gap between regulatory policies and system-level uncertainty, this work could shape future developments in the field, offering a blueprint for regions striving to balance economic and environmental goals.